Overview

Dataset statistics

Number of variables78
Number of observations20835
Missing cells0
Missing cells (%)0.0%
Duplicate rows2198
Duplicate rows (%)10.5%
Total size in memory13.1 MiB
Average record size in memory657.4 B

Variable types

Categorical70
Numeric8

Alerts

tetra-top has constant value "0"Constant
pla-polylactic-acid has constant value "0"Constant
Dataset has 2198 (10.5%) duplicate rowsDuplicates
est_co2_agriculture is highly overall correlated with est_co2_processingHigh correlation
est_co2_consumption is highly overall correlated with est_co2_distributionHigh correlation
est_co2_distribution is highly overall correlated with est_co2_consumptionHigh correlation
est_co2_processing is highly overall correlated with est_co2_agricultureHigh correlation
plastic is highly overall correlated with unknownHigh correlation
pure-pak is highly overall correlated with pure-pak-classicHigh correlation
o-7-other-plastics is highly overall correlated with other-plasticsHigh correlation
non-corrugated-cardboard is highly overall correlated with corrugated-cardboardHigh correlation
other-plastics is highly overall correlated with o-7-other-plasticsHigh correlation
pure-pak-classic is highly overall correlated with pure-pakHigh correlation
tetra-brik-aseptic is highly overall correlated with tetra-brikHigh correlation
corrugated-cardboard is highly overall correlated with non-corrugated-cardboardHigh correlation
plastic-aluminium is highly overall correlated with paper-and-cardboard-plastic-aluminiumHigh correlation
paper-and-cardboard-plastic-aluminium is highly overall correlated with plastic-aluminiumHigh correlation
unknown is highly overall correlated with plasticHigh correlation
tetra-brik is highly overall correlated with tetra-brik-asepticHigh correlation
non_recyclable_and_non_biodegradable_materials_count is highly imbalanced (61.2%)Imbalance
recycled-cardboard is highly imbalanced (99.9%)Imbalance
pure-pak is highly imbalanced (99.6%)Imbalance
plastic,en:metal is highly imbalanced (99.8%)Imbalance
sig is highly imbalanced (99.7%)Imbalance
light-aluminium is highly imbalanced (97.0%)Imbalance
pe-7-polyethylene is highly imbalanced (99.9%)Imbalance
rpet-recycled-polyethylene-terephthalate is highly imbalanced (99.9%)Imbalance
pet-colored is highly imbalanced (99.9%)Imbalance
pet-polyethylene-terephthalate is highly imbalanced (96.6%)Imbalance
other-paper is highly imbalanced (99.9%)Imbalance
hdpe-high-density-polyethylene is highly imbalanced (98.3%)Imbalance
clear-glass is highly imbalanced (99.2%)Imbalance
metal is highly imbalanced (70.4%)Imbalance
paper is highly imbalanced (81.4%)Imbalance
o-7-other-plastics is highly imbalanced (99.7%)Imbalance
baking-paper is highly imbalanced (99.9%)Imbalance
multilayer-composite is highly imbalanced (99.7%)Imbalance
recycled-plastic is highly imbalanced (99.7%)Imbalance
non-corrugated-cardboard is highly imbalanced (98.3%)Imbalance
other-plastics is highly imbalanced (99.2%)Imbalance
cork is highly imbalanced (99.2%)Imbalance
brown-glass is highly imbalanced (99.9%)Imbalance
pp-polypropylene is highly imbalanced (92.4%)Imbalance
pure-pak-classic is highly imbalanced (99.6%)Imbalance
italpack is highly imbalanced (99.9%)Imbalance
ldpe-4-low-density-polyethylene is highly imbalanced (99.9%)Imbalance
tetra-brik-aseptic is highly imbalanced (98.9%)Imbalance
pet-opaque is highly imbalanced (99.9%)Imbalance
22 is highly imbalanced (99.9%)Imbalance
fsc-cardboard is highly imbalanced (99.7%)Imbalance
paper-and-fibreboard-miscellaneous-metals is highly imbalanced (99.9%)Imbalance
green-glass is highly imbalanced (99.9%)Imbalance
tetra-pak is highly imbalanced (94.3%)Imbalance
opaque-pet is highly imbalanced (99.9%)Imbalance
fsc-paper is highly imbalanced (99.9%)Imbalance
corrugated-cardboard is highly imbalanced (98.1%)Imbalance
heavy-aluminium is highly imbalanced (87.7%)Imbalance
recycled-paper is highly imbalanced (99.9%)Imbalance
pe-polyethylene is highly imbalanced (99.9%)Imbalance
pet-transparent is highly imbalanced (99.9%)Imbalance
ps-polystyrene is highly imbalanced (98.3%)Imbalance
plastic-aluminium is highly imbalanced (99.7%)Imbalance
fabric is highly imbalanced (99.7%)Imbalance
paper-and-cardboard-plastic-aluminium is highly imbalanced (99.7%)Imbalance
pp-5-polypropylene is highly imbalanced (97.5%)Imbalance
tin-plated-steel is highly imbalanced (99.7%)Imbalance
recyclable-plastic is highly imbalanced (99.7%)Imbalance
pet-1-polyethylene-terephthalate is highly imbalanced (99.4%)Imbalance
composite-material is highly imbalanced (99.9%)Imbalance
90 is highly imbalanced (99.3%)Imbalance
paperboard is highly imbalanced (97.5%)Imbalance
glass is highly imbalanced (72.0%)Imbalance
ldpe-low-density-polyethylene is highly imbalanced (99.2%)Imbalance
ps-6-polystyrene is highly imbalanced (99.7%)Imbalance
tetra-rex is highly imbalanced (99.9%)Imbalance
kraft-paper is highly imbalanced (99.9%)Imbalance
40 is highly imbalanced (99.9%)Imbalance
wood is highly imbalanced (98.0%)Imbalance
paper-and-plastic is highly imbalanced (99.6%)Imbalance
transparent-pet is highly imbalanced (99.9%)Imbalance
elopak is highly imbalanced (99.7%)Imbalance
steel is highly imbalanced (97.2%)Imbalance
hdpe-2-high-density-polyethylene is highly imbalanced (99.3%)Imbalance
tetra-brik is highly imbalanced (98.0%)Imbalance
est_co2_consumption has 8405 (40.3%) zerosZeros
est_co2_packaging has 546 (2.6%) zerosZeros
est_co2_processing has 1971 (9.5%) zerosZeros
main_ingredient has 2793 (13.4%) zerosZeros

Reproduction

Analysis started2023-06-19 06:35:13.658632
Analysis finished2023-06-19 06:36:04.420039
Duration50.76 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
1.0
15109 
0.0
5569 
2.0
 
148
3.0
 
8
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters62505
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 15109
72.5%
0.0 5569
 
26.7%
2.0 148
 
0.7%
3.0 8
 
< 0.1%
4.0 1
 
< 0.1%

Length

2023-06-19T06:36:04.938702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:05.156841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 15109
72.5%
0.0 5569
 
26.7%
2.0 148
 
0.7%
3.0 8
 
< 0.1%
4.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 26404
42.2%
. 20835
33.3%
1 15109
24.2%
2 148
 
0.2%
3 8
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41670
66.7%
Other Punctuation 20835
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26404
63.4%
1 15109
36.3%
2 148
 
0.4%
3 8
 
< 0.1%
4 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 20835
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 62505
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26404
42.2%
. 20835
33.3%
1 15109
24.2%
2 148
 
0.2%
3 8
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26404
42.2%
. 20835
33.3%
1 15109
24.2%
2 148
 
0.2%
3 8
 
< 0.1%
4 1
 
< 0.1%

est_co2_agriculture
Real number (ℝ)

Distinct829
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7906292
Minimum0
Maximum51.221154
Zeros25
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size841.6 KiB
2023-06-19T06:36:05.386271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.081751098
Q10.72818111
median1.5302736
Q33.9771706
95-th percentile9.2229536
Maximum51.221154
Range51.221154
Interquartile range (IQR)3.2489895

Descriptive statistics

Standard deviation3.6572982
Coefficient of variation (CV)1.310564
Kurtosis16.063382
Mean2.7906292
Median Absolute Deviation (MAD)1.2015975
Skewness3.341084
Sum58142.76
Variance13.37583
MonotonicityNot monotonic
2023-06-19T06:36:05.640805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.066498 915
 
4.4%
2.2396004 745
 
3.6%
4.1425839 601
 
2.9%
4.456254 400
 
1.9%
2.3889426 397
 
1.9%
3.1288494 374
 
1.8%
0.023049705 361
 
1.7%
1.4638909 355
 
1.7%
3.9771706 340
 
1.6%
5.8902263 330
 
1.6%
Other values (819) 16017
76.9%
ValueCountFrequency (%)
0 25
 
0.1%
0.0062273465 24
 
0.1%
0.0097530244 2
 
< 0.1%
0.0097530244 1
 
< 0.1%
0.014448574 25
 
0.1%
0.015849892 1
 
< 0.1%
0.019746487 1
 
< 0.1%
0.023049705 361
1.7%
0.0318733 2
 
< 0.1%
0.031880378 25
 
0.1%
ValueCountFrequency (%)
51.221154 1
 
< 0.1%
42.199811 1
 
< 0.1%
34.387569 6
 
< 0.1%
32.437523 1
 
< 0.1%
29.803955 1
 
< 0.1%
27.221359 70
0.3%
23.726495 15
 
0.1%
23.17229 7
 
< 0.1%
22.846441 6
 
< 0.1%
22.552693 7
 
< 0.1%

est_co2_consumption
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.019209533
Minimum0
Maximum1.2859264
Zeros8405
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size841.6 KiB
2023-06-19T06:36:05.904430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0066875618
Q30.010930764
95-th percentile0.092756569
Maximum1.2859264
Range1.2859264
Interquartile range (IQR)0.010930764

Descriptive statistics

Standard deviation0.062840042
Coefficient of variation (CV)3.2712946
Kurtosis159.80653
Mean0.019209533
Median Absolute Deviation (MAD)0.0066875618
Skewness10.478929
Sum400.23063
Variance0.0039488709
MonotonicityNot monotonic
2023-06-19T06:36:06.160298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8405
40.3%
0.0066875618 6978
33.5%
0.025304288 1073
 
5.1%
0.017733084 717
 
3.4%
0.092756569 669
 
3.2%
0.011045523 447
 
2.1%
0.080370398 431
 
2.1%
0.011257129 182
 
0.9%
0.011257129 179
 
0.9%
0.08872985 168
 
0.8%
Other values (68) 1586
 
7.6%
ValueCountFrequency (%)
0 8405
40.3%
0.00027759677 109
 
0.5%
0.0049059334 1
 
< 0.1%
0.0049059334 21
 
0.1%
0.0049489679 13
 
0.1%
0.0057371351 2
 
< 0.1%
0.0066875618 6978
33.5%
0.0099386422 77
 
0.4%
0.0099427296 3
 
< 0.1%
0.010930764 166
 
0.8%
ValueCountFrequency (%)
1.2859264 11
 
0.1%
0.99119502 24
 
0.1%
0.55764343 1
 
< 0.1%
0.44915369 6
 
< 0.1%
0.40778319 61
0.3%
0.32148159 143
0.7%
0.28217828 11
 
0.1%
0.225467 3
 
< 0.1%
0.21433375 8
 
< 0.1%
0.21432106 48
 
0.2%

est_co2_distribution
Real number (ℝ)

Distinct191
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.028537999
Minimum0
Maximum0.20574244
Zeros62
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size841.6 KiB
2023-06-19T06:36:06.415723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0048545734
Q10.017321188
median0.022706535
Q30.037049036
95-th percentile0.064524479
Maximum0.20574244
Range0.20574244
Interquartile range (IQR)0.019727848

Descriptive statistics

Standard deviation0.01697466
Coefficient of variation (CV)0.59480906
Kurtosis11.780732
Mean0.028537999
Median Absolute Deviation (MAD)0.012800843
Skewness2.1375139
Sum594.58921
Variance0.0002881391
MonotonicityNot monotonic
2023-06-19T06:36:06.654526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.019530673 2636
 
12.7%
0.037393191 2270
 
10.9%
0.036974137 1854
 
8.9%
0.03557378 1723
 
8.3%
0.017321188 1546
 
7.4%
0.015708527 941
 
4.5%
0.039628148 876
 
4.2%
0.0048545734 715
 
3.4%
0.019530673 656
 
3.1%
0.064524479 653
 
3.1%
Other values (181) 6965
33.4%
ValueCountFrequency (%)
0 62
 
0.3%
0.00015708527 182
0.9%
0.00015708527 179
0.9%
0.00030150552 20
 
0.1%
0.00030150552 5
 
< 0.1%
0.00038295438 1
 
< 0.1%
0.0008681614 13
 
0.1%
0.0009162784 11
 
0.1%
0.0022977281 1
 
< 0.1%
0.0022977281 108
0.5%
ValueCountFrequency (%)
0.20574244 3
 
< 0.1%
0.19791378 1
 
< 0.1%
0.16886379 10
 
< 0.1%
0.15197843 17
0.1%
0.15065127 7
 
< 0.1%
0.14789655 24
0.1%
0.14705472 6
 
< 0.1%
0.11741477 35
0.2%
0.1067407 6
 
< 0.1%
0.10530838 3
 
< 0.1%

est_co2_packaging
Real number (ℝ)

Distinct388
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25841315
Minimum0
Maximum9.7697831
Zeros546
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size841.6 KiB
2023-06-19T06:36:06.904454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.006798904
Q10.12376644
median0.22613436
Q30.28154442
95-th percentile0.49851972
Maximum9.7697831
Range9.7697831
Interquartile range (IQR)0.15777798

Descriptive statistics

Standard deviation0.43736862
Coefficient of variation (CV)1.6925169
Kurtosis246.88041
Mean0.25841315
Median Absolute Deviation (MAD)0.05546156
Skewness13.848944
Sum5384.0379
Variance0.19129131
MonotonicityNot monotonic
2023-06-19T06:36:07.144309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.27564225 1143
 
5.5%
0.26266115 1064
 
5.1%
0.18066363 949
 
4.6%
0.10010411 936
 
4.5%
0.10429359 855
 
4.1%
0.18055336 833
 
4.0%
0.28159592 738
 
3.5%
0.18574567 642
 
3.1%
0.28154442 565
 
2.7%
0 546
 
2.6%
Other values (378) 12564
60.3%
ValueCountFrequency (%)
0 546
2.6%
0.0026356466 361
1.7%
0.0055324428 3
 
< 0.1%
0.0055324428 10
 
< 0.1%
0.005839073 11
 
0.1%
0.0061347374 15
 
0.1%
0.0061347374 32
 
0.2%
0.0062820739 1
 
< 0.1%
0.0063265585 27
 
0.1%
0.0063277746 1
 
< 0.1%
ValueCountFrequency (%)
9.7697831 20
 
0.1%
4.136523 118
0.6%
2.3153095 1
 
< 0.1%
1.4175933 6
 
< 0.1%
1.1988433 15
 
0.1%
1.102569 18
 
0.1%
1.0767333 6
 
< 0.1%
0.7399657 31
 
0.1%
0.72778284 4
 
< 0.1%
0.71469869 14
 
0.1%

est_co2_processing
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct697
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0692234
Minimum0
Maximum19.052315
Zeros1971
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size841.6 KiB
2023-06-19T06:36:07.393068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.15010908
median0.25169407
Q30.66562203
95-th percentile6.8335411
Maximum19.052315
Range19.052315
Interquartile range (IQR)0.51551295

Descriptive statistics

Standard deviation2.6863442
Coefficient of variation (CV)2.5124254
Kurtosis20.56191
Mean1.0692234
Median Absolute Deviation (MAD)0.16295294
Skewness4.3712732
Sum22277.271
Variance7.2164449
MonotonicityNot monotonic
2023-06-19T06:36:07.650439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1971
 
9.5%
0.77051126 745
 
3.6%
0.82416344 601
 
2.9%
0.21551441 592
 
2.8%
0.39195814 430
 
2.1%
0.26259642 400
 
1.9%
0.22878446 397
 
1.9%
0.011248836 393
 
1.9%
2.4887872 374
 
1.8%
15.259235 355
 
1.7%
Other values (687) 14577
70.0%
ValueCountFrequency (%)
0 1971
9.5%
0.00018543333 98
 
0.5%
0.0035409025 3
 
< 0.1%
0.0063536972 7
 
< 0.1%
0.0075366852 5
 
< 0.1%
0.0085032768 31
 
0.1%
0.011248836 393
 
1.9%
0.011305648 4
 
< 0.1%
0.011305648 32
 
0.2%
0.011635582 1
 
< 0.1%
ValueCountFrequency (%)
19.052315 93
 
0.4%
15.259235 355
1.7%
8.6508843 15
 
0.1%
8.3337431 340
1.6%
6.9985445 29
 
0.1%
6.9985445 148
0.7%
6.9443507 7
 
< 0.1%
6.8335411 73
 
0.4%
6.4912047 23
 
0.1%
6.220799 1
 
< 0.1%

est_co2_transportation
Real number (ℝ)

Distinct758
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25019754
Minimum0.0022748854
Maximum7.799588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size841.6 KiB
2023-06-19T06:36:07.927415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0022748854
5-th percentile0.11801454
Q10.18416821
median0.21340031
Q30.2662668
95-th percentile0.45621559
Maximum7.799588
Range7.7973131
Interquartile range (IQR)0.08209859

Descriptive statistics

Standard deviation0.20108764
Coefficient of variation (CV)0.80371551
Kurtosis483.65017
Mean0.25019754
Median Absolute Deviation (MAD)0.04173385
Skewness14.736809
Sum5212.8657
Variance0.04043624
MonotonicityNot monotonic
2023-06-19T06:36:08.183427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21150363 1092
 
5.2%
0.25287373 738
 
3.5%
0.23303866 601
 
2.9%
0.19722452 592
 
2.8%
0.36622515 430
 
2.1%
0.13545355 395
 
1.9%
0.0022748854 361
 
1.7%
0.26554707 355
 
1.7%
0.30877176 340
 
1.6%
0.20550955 330
 
1.6%
Other values (748) 15601
74.9%
ValueCountFrequency (%)
0.0022748854 361
1.7%
0.0060532488 25
 
0.1%
0.010626895 7
 
< 0.1%
0.01289847 1
 
< 0.1%
0.014644818 3
 
< 0.1%
0.015906686 2
 
< 0.1%
0.01641097 2
 
< 0.1%
0.019057545 1
 
< 0.1%
0.019057545 108
 
0.5%
0.026611056 2
 
< 0.1%
ValueCountFrequency (%)
7.799588 5
 
< 0.1%
1.4966165 18
 
0.1%
1.4956964 36
0.2%
1.433752 1
 
< 0.1%
1.4153897 5
 
< 0.1%
1.3716592 1
 
< 0.1%
1.3716592 7
 
< 0.1%
1.3542844 7
 
< 0.1%
1.1741883 45
0.2%
1.1225833 50
0.2%

main_category
Real number (ℝ)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.643868
Minimum0
Maximum27
Zeros33
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size841.6 KiB
2023-06-19T06:36:08.451045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q18
median13
Q313
95-th percentile21
Maximum27
Range27
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.7836233
Coefficient of variation (CV)0.37833542
Kurtosis-0.57730321
Mean12.643868
Median Absolute Deviation (MAD)4
Skewness0.49269559
Sum263435
Variance22.883052
MonotonicityNot monotonic
2023-06-19T06:36:08.653880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
13 8098
38.9%
7 3444
16.5%
21 2541
 
12.2%
8 2479
 
11.9%
16 1069
 
5.1%
9 1015
 
4.9%
20 841
 
4.0%
6 333
 
1.6%
17 160
 
0.8%
10 120
 
0.6%
Other values (17) 735
 
3.5%
ValueCountFrequency (%)
0 33
 
0.2%
1 4
 
< 0.1%
2 34
 
0.2%
3 1
 
< 0.1%
4 6
 
< 0.1%
6 333
 
1.6%
7 3444
16.5%
8 2479
11.9%
9 1015
 
4.9%
10 120
 
0.6%
ValueCountFrequency (%)
27 24
 
0.1%
26 41
 
0.2%
25 4
 
< 0.1%
24 21
 
0.1%
23 106
 
0.5%
22 52
 
0.2%
21 2541
12.2%
20 841
 
4.0%
19 70
 
0.3%
18 5
 
< 0.1%

main_ingredient
Real number (ℝ)

Distinct910
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean458.52609
Minimum0
Maximum935
Zeros2793
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size841.6 KiB
2023-06-19T06:36:08.883784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1265
median526
Q3654
95-th percentile861
Maximum935
Range935
Interquartile range (IQR)389

Descriptive statistics

Standard deviation269.21851
Coefficient of variation (CV)0.58713892
Kurtosis-0.80402393
Mean458.52609
Median Absolute Deviation (MAD)151
Skewness-0.35638124
Sum9553391
Variance72478.605
MonotonicityNot monotonic
2023-06-19T06:36:09.126692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
526 5345
25.7%
0 2793
 
13.4%
677 1276
 
6.1%
522 1150
 
5.5%
855 1101
 
5.3%
456 366
 
1.8%
895 278
 
1.3%
868 272
 
1.3%
625 269
 
1.3%
221 265
 
1.3%
Other values (900) 7720
37.1%
ValueCountFrequency (%)
0 2793
13.4%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
935 3
 
< 0.1%
934 3
 
< 0.1%
933 49
0.2%
932 3
 
< 0.1%
931 6
 
< 0.1%
930 9
 
< 0.1%
929 2
 
< 0.1%
928 1
 
< 0.1%
927 1
 
< 0.1%
926 3
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:09.336056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:09.532875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

plastic
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
13933 
1
6902 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 13933
66.9%
1 6902
33.1%

Length

2023-06-19T06:36:09.697533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:09.901695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13933
66.9%
1 6902
33.1%

Most occurring characters

ValueCountFrequency (%)
0 13933
66.9%
1 6902
33.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13933
66.9%
1 6902
33.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13933
66.9%
1 6902
33.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13933
66.9%
1 6902
33.1%

pure-pak
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20829 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20829
> 99.9%
1 6
 
< 0.1%

Length

2023-06-19T06:36:10.063532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:10.261672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20829
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20829
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20829
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20829
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20829
> 99.9%
1 6
 
< 0.1%

plastic,en:metal
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20832 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20832
> 99.9%
1 3
 
< 0.1%

Length

2023-06-19T06:36:10.430221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:10.630162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20832
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20832
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20832
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20832
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20832
> 99.9%
1 3
 
< 0.1%

sig
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20830 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Length

2023-06-19T06:36:10.797932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:10.991464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

light-aluminium
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20771 
1
 
64

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20771
99.7%
1 64
 
0.3%

Length

2023-06-19T06:36:11.155645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:11.359635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20771
99.7%
1 64
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 20771
99.7%
1 64
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20771
99.7%
1 64
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20771
99.7%
1 64
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20771
99.7%
1 64
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:11.528398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:11.730298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:11.902225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:12.095082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

pet-colored
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:12.265692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:12.465040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20761 
1
 
74

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20761
99.6%
1 74
 
0.4%

Length

2023-06-19T06:36:12.625509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:12.824240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20761
99.6%
1 74
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 20761
99.6%
1 74
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20761
99.6%
1 74
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20761
99.6%
1 74
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20761
99.6%
1 74
 
0.4%

other-paper
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:12.987056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:13.181351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20803 
1
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20803
99.8%
1 32
 
0.2%

Length

2023-06-19T06:36:13.345725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:13.554064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20803
99.8%
1 32
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 20803
99.8%
1 32
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20803
99.8%
1 32
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20803
99.8%
1 32
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20803
99.8%
1 32
 
0.2%

clear-glass
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20822 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Length

2023-06-19T06:36:13.723177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:13.944637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

metal
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
19748 
1
 
1087

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19748
94.8%
1 1087
 
5.2%

Length

2023-06-19T06:36:14.612486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:15.032567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19748
94.8%
1 1087
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 19748
94.8%
1 1087
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19748
94.8%
1 1087
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19748
94.8%
1 1087
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19748
94.8%
1 1087
 
5.2%

paper
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20244 
1
 
591

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20244
97.2%
1 591
 
2.8%

Length

2023-06-19T06:36:15.389472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:15.778789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20244
97.2%
1 591
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 20244
97.2%
1 591
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20244
97.2%
1 591
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20244
97.2%
1 591
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20244
97.2%
1 591
 
2.8%

o-7-other-plastics
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20830 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Length

2023-06-19T06:36:16.121036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:16.516354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

baking-paper
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:16.890792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:17.302014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20831 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Length

2023-06-19T06:36:17.471378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:17.669550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

recycled-plastic
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20830 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Length

2023-06-19T06:36:17.836133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:18.046945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

non-corrugated-cardboard
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20802 
1
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20802
99.8%
1 33
 
0.2%

Length

2023-06-19T06:36:18.214771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:18.424005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20802
99.8%
1 33
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 20802
99.8%
1 33
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20802
99.8%
1 33
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20802
99.8%
1 33
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20802
99.8%
1 33
 
0.2%

other-plastics
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20822 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Length

2023-06-19T06:36:18.590930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:18.783253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

cork
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20821 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20821
99.9%
1 14
 
0.1%

Length

2023-06-19T06:36:18.941090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:19.135062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20821
99.9%
1 14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 20821
99.9%
1 14
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20821
99.9%
1 14
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20821
99.9%
1 14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20821
99.9%
1 14
 
0.1%

brown-glass
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20833 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Length

2023-06-19T06:36:19.292492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:19.489577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

pp-polypropylene
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20643 
1
 
192

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20643
99.1%
1 192
 
0.9%

Length

2023-06-19T06:36:19.651577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:19.842125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20643
99.1%
1 192
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 20643
99.1%
1 192
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20643
99.1%
1 192
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20643
99.1%
1 192
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20643
99.1%
1 192
 
0.9%

pure-pak-classic
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20829 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20829
> 99.9%
1 6
 
< 0.1%

Length

2023-06-19T06:36:20.005292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:20.201693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20829
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20829
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20829
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20829
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20829
> 99.9%
1 6
 
< 0.1%

italpack
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:20.369297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:20.566087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20833 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Length

2023-06-19T06:36:20.729155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:20.922272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

tetra-brik-aseptic
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20816 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20816
99.9%
1 19
 
0.1%

Length

2023-06-19T06:36:21.087492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:21.284786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20816
99.9%
1 19
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 20816
99.9%
1 19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20816
99.9%
1 19
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20816
99.9%
1 19
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20816
99.9%
1 19
 
0.1%

pet-opaque
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:21.446694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:21.649064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

22
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:21.808048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:21.994471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

fsc-cardboard
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20831 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Length

2023-06-19T06:36:22.164455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:22.357553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:22.515918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:22.712651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

green-glass
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20833 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Length

2023-06-19T06:36:22.872306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:23.068918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

tetra-top
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20835 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20835
100.0%

Length

2023-06-19T06:36:23.245789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:23.451914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20835
100.0%

Most occurring characters

ValueCountFrequency (%)
0 20835
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20835
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20835
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20835
100.0%

tetra-pak
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20698 
1
 
137

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20698
99.3%
1 137
 
0.7%

Length

2023-06-19T06:36:23.621900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:23.817183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20698
99.3%
1 137
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 20698
99.3%
1 137
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20698
99.3%
1 137
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20698
99.3%
1 137
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20698
99.3%
1 137
 
0.7%

opaque-pet
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:23.975989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:24.182028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

fsc-paper
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:24.352418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:24.569444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

corrugated-cardboard
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20797 
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20797
99.8%
1 38
 
0.2%

Length

2023-06-19T06:36:24.734408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:24.926680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20797
99.8%
1 38
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 20797
99.8%
1 38
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20797
99.8%
1 38
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20797
99.8%
1 38
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20797
99.8%
1 38
 
0.2%

heavy-aluminium
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20485 
1
 
350

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20485
98.3%
1 350
 
1.7%

Length

2023-06-19T06:36:25.087173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:25.287974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20485
98.3%
1 350
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 20485
98.3%
1 350
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20485
98.3%
1 350
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20485
98.3%
1 350
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20485
98.3%
1 350
 
1.7%

recycled-paper
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20833 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Length

2023-06-19T06:36:25.457614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:25.660175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

pe-polyethylene
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20833 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Length

2023-06-19T06:36:25.823045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:26.021277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

pet-transparent
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20833 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Length

2023-06-19T06:36:26.199275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:26.405869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

ps-polystyrene
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20802 
1
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20802
99.8%
1 33
 
0.2%

Length

2023-06-19T06:36:26.573318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:26.777152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20802
99.8%
1 33
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 20802
99.8%
1 33
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20802
99.8%
1 33
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20802
99.8%
1 33
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20802
99.8%
1 33
 
0.2%

plastic-aluminium
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20830 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Length

2023-06-19T06:36:26.945609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:27.148601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

fabric
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20831 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Length

2023-06-19T06:36:27.343019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:28.401176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

paper-and-cardboard-plastic-aluminium
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20831 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Length

2023-06-19T06:36:28.892787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:30.016100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20783 
1
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20783
99.8%
1 52
 
0.2%

Length

2023-06-19T06:36:30.437099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:31.170090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20783
99.8%
1 52
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 20783
99.8%
1 52
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20783
99.8%
1 52
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20783
99.8%
1 52
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20783
99.8%
1 52
 
0.2%

unknown
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
1
11439 
0
9396 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 11439
54.9%
0 9396
45.1%

Length

2023-06-19T06:36:31.344267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:32.050950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 11439
54.9%
0 9396
45.1%

Most occurring characters

ValueCountFrequency (%)
1 11439
54.9%
0 9396
45.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11439
54.9%
0 9396
45.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11439
54.9%
0 9396
45.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11439
54.9%
0 9396
45.1%

tin-plated-steel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20831 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Length

2023-06-19T06:36:32.220559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:32.433417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20831 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Length

2023-06-19T06:36:32.594169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:32.798698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20825 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20825
> 99.9%
1 10
 
< 0.1%

Length

2023-06-19T06:36:32.961136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:33.158013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20825
> 99.9%
1 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20825
> 99.9%
1 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20825
> 99.9%
1 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20825
> 99.9%
1 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20825
> 99.9%
1 10
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:33.321561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:33.531314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

90
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20823 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20823
99.9%
1 12
 
0.1%

Length

2023-06-19T06:36:33.698675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:33.900129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20823
99.9%
1 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 20823
99.9%
1 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20823
99.9%
1 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20823
99.9%
1 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20823
99.9%
1 12
 
0.1%

paperboard
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20784 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20784
99.8%
1 51
 
0.2%

Length

2023-06-19T06:36:34.061702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:34.260065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20784
99.8%
1 51
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 20784
99.8%
1 51
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20784
99.8%
1 51
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20784
99.8%
1 51
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20784
99.8%
1 51
 
0.2%

glass
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
19825 
1
 
1010

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 19825
95.2%
1 1010
 
4.8%

Length

2023-06-19T06:36:34.434157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:34.638013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19825
95.2%
1 1010
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 19825
95.2%
1 1010
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19825
95.2%
1 1010
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19825
95.2%
1 1010
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19825
95.2%
1 1010
 
4.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20822 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Length

2023-06-19T06:36:34.799328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:34.992098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20822
99.9%
1 13
 
0.1%

ps-6-polystyrene
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20831 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Length

2023-06-19T06:36:35.150143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:35.343716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20831
> 99.9%
1 4
 
< 0.1%

tetra-rex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:35.518505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:35.718851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

cardboard
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
17463 
1
3372 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17463
83.8%
1 3372
 
16.2%

Length

2023-06-19T06:36:35.885615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:36.087712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 17463
83.8%
1 3372
 
16.2%

Most occurring characters

ValueCountFrequency (%)
0 17463
83.8%
1 3372
 
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17463
83.8%
1 3372
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17463
83.8%
1 3372
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17463
83.8%
1 3372
 
16.2%

kraft-paper
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:36.267346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:36.484990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

40
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20834 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Length

2023-06-19T06:36:36.656916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:36.857435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20834
> 99.9%
1 1
 
< 0.1%

wood
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20796 
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20796
99.8%
1 39
 
0.2%

Length

2023-06-19T06:36:37.021950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:37.231898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20796
99.8%
1 39
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 20796
99.8%
1 39
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20796
99.8%
1 39
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20796
99.8%
1 39
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20796
99.8%
1 39
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20828 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20828
> 99.9%
1 7
 
< 0.1%

Length

2023-06-19T06:36:37.400601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:37.618505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20828
> 99.9%
1 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20828
> 99.9%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20828
> 99.9%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20828
> 99.9%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20828
> 99.9%
1 7
 
< 0.1%

transparent-pet
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20833 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Length

2023-06-19T06:36:37.785766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:37.981457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20833
> 99.9%
1 2
 
< 0.1%

elopak
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20830 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Length

2023-06-19T06:36:38.140146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:38.335485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20830
> 99.9%
1 5
 
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20835 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20835
100.0%

Length

2023-06-19T06:36:38.501414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:38.699986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20835
100.0%

Most occurring characters

ValueCountFrequency (%)
0 20835
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20835
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20835
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20835
100.0%

steel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20777 
1
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20777
99.7%
1 58
 
0.3%

Length

2023-06-19T06:36:38.854139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:39.055765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20777
99.7%
1 58
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 20777
99.7%
1 58
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20777
99.7%
1 58
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20777
99.7%
1 58
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20777
99.7%
1 58
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20824 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20824
99.9%
1 11
 
0.1%

Length

2023-06-19T06:36:39.219697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:39.438331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20824
99.9%
1 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 20824
99.9%
1 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20824
99.9%
1 11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20824
99.9%
1 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20824
99.9%
1 11
 
0.1%

tetra-brik
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size841.6 KiB
0
20796 
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20796
99.8%
1 39
 
0.2%

Length

2023-06-19T06:36:39.634572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T06:36:39.852323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20796
99.8%
1 39
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 20796
99.8%
1 39
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20796
99.8%
1 39
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20796
99.8%
1 39
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20796
99.8%
1 39
 
0.2%

Interactions

2023-06-19T06:35:59.802121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:45.919506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:48.018991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:50.747199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:52.528343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:54.194651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:56.323730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:58.114376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:59.988467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:46.127934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:48.340772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:51.048839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:52.740253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:54.422180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:56.543089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:58.315150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:36:00.187061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:46.577749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:48.686526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:51.258853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:52.938633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:54.665922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:56.767742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:58.548311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:36:00.365959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:46.770922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:49.022575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:51.471983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:53.151175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:55.193372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:56.969038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:58.755477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:36:00.581794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:46.973360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:49.351539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:51.681674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:53.356360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:55.419082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:57.167159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:58.945285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:36:00.815943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:47.219275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:49.717331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:51.902693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:53.586006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:55.661109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:57.440757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:59.178552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:36:01.045493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:47.451982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:50.080843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:52.114123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:53.799948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:55.895894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:57.679114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:59.401701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:36:01.328616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:47.689044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:50.397668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:52.331844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:53.999538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:56.109992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:57.902593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-19T06:35:59.619200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-19T06:36:40.152720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
est_co2_agricultureest_co2_consumptionest_co2_distributionest_co2_packagingest_co2_processingest_co2_transportationmain_categorymain_ingredientnon_recyclable_and_non_biodegradable_materials_countrecycled-cardboardplasticpure-pakplastic,en:metalsiglight-aluminiumpe-7-polyethylenerpet-recycled-polyethylene-terephthalatepet-coloredpet-polyethylene-terephthalateother-paperhdpe-high-density-polyethyleneclear-glassmetalpapero-7-other-plasticsbaking-papermultilayer-compositerecycled-plasticnon-corrugated-cardboardother-plasticscorkbrown-glasspp-polypropylenepure-pak-classicitalpackldpe-4-low-density-polyethylenetetra-brik-asepticpet-opaque22fsc-cardboardpaper-and-fibreboard-miscellaneous-metalsgreen-glasstetra-pakopaque-petfsc-papercorrugated-cardboardheavy-aluminiumrecycled-paperpe-polyethylenepet-transparentps-polystyreneplastic-aluminiumfabricpaper-and-cardboard-plastic-aluminiumpp-5-polypropyleneunknowntin-plated-steelrecyclable-plasticpet-1-polyethylene-terephthalatecomposite-material90paperboardglassldpe-low-density-polyethyleneps-6-polystyrenetetra-rexcardboardkraft-paper40woodpaper-and-plastictransparent-petelopaksteelhdpe-2-high-density-polyethylenetetra-brik
est_co2_agriculture1.000-0.0540.1960.1700.5090.2400.101-0.0050.0260.0000.0520.0000.0000.0120.0000.0000.0000.0000.0150.0000.0000.0500.0390.0140.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0280.0000.0000.0000.0070.0000.0130.0110.0330.0000.0000.0000.0000.0000.0070.0880.0000.0000.0000.0740.0000.0000.0000.0000.0000.0000.0000.0000.000
est_co2_consumption-0.0541.0000.5810.015-0.2570.2240.326-0.0160.0030.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0420.0000.0000.1280.0000.0000.0000.0000.0000.000
est_co2_distribution0.1960.5811.0000.2430.0270.3400.221-0.0120.0320.0190.0300.0000.0000.0000.0430.0000.0000.0000.0060.0000.0000.0000.0610.0630.0000.0000.0000.0000.0210.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0190.0000.0230.0410.0000.0000.0000.0380.0000.0000.0000.0040.0730.0000.0000.0000.0000.0000.0450.0540.0000.0000.0000.1050.0000.0000.0940.0000.0000.0000.0000.0140.023
est_co2_packaging0.1700.0150.2431.0000.1610.1560.2360.0010.0270.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1530.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0340.0000.0000.0930.0000.0000.0000.0150.0000.000
est_co2_processing0.509-0.2570.0270.1611.0000.173-0.034-0.0050.0590.0000.0920.0000.0000.0000.2380.0000.0000.1020.0000.0000.0000.0450.0390.1500.0410.0500.0000.0000.0440.0330.0000.0000.1440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0410.2400.0000.0000.0000.0110.0000.0000.0000.1050.0580.0480.0000.0000.0000.0000.0000.0800.0000.0000.0000.1110.0000.0000.0000.0340.0000.0000.0120.0120.000
est_co2_transportation0.2400.2240.3400.1560.1731.0000.294-0.0100.0240.0000.0120.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0550.0260.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0150.0000.000
main_category0.1010.3260.2210.236-0.0340.2941.000-0.0150.0640.0200.1190.0030.0000.0290.0780.0000.0000.0000.0450.0000.0180.0290.1100.0890.0000.0000.0000.0000.0260.0000.0240.0000.0940.0030.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0260.1070.0000.0000.0000.0540.0000.0000.0000.0240.0860.0000.0000.0000.0000.0160.0580.2680.0000.0230.0000.1520.0000.0000.0460.0000.0000.0000.0310.0000.030
main_ingredient-0.005-0.016-0.0120.001-0.005-0.010-0.0151.0000.0000.0000.0060.0140.0020.0000.0050.0000.0000.0000.0000.0000.0000.0080.0000.0000.0290.0000.0030.0000.0240.0230.0000.0000.0120.0140.0000.0170.0000.0270.0000.0000.0000.0110.0140.0000.0000.0220.0190.0160.0210.0130.0000.0000.0080.0000.0000.0140.0000.0000.0000.0000.0000.0100.0170.0000.0000.0000.0060.0000.0000.0120.0000.0000.0000.0000.0000.000
non_recyclable_and_non_biodegradable_materials_count0.0260.0030.0320.0270.0590.0240.0640.0001.0000.0000.2380.0300.0140.0000.0810.0000.0000.0000.0940.0000.0490.0340.3390.1820.0000.0000.0800.0000.0620.0000.0410.0000.1530.0300.0000.0000.0120.0000.0000.0000.0000.0080.0640.0000.0000.0600.1450.0080.0000.0080.0700.0340.0000.0380.0820.2750.0180.0000.0220.0000.1160.0350.3570.0250.0000.0000.2530.0000.0000.0230.0260.0080.0220.0820.0250.023
recycled-cardboard0.0000.0000.0190.0000.0000.0000.0200.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
plastic0.0520.0050.0300.0510.0920.0120.1190.0060.2380.0001.0000.0000.0110.0030.0080.0000.0000.0000.0100.0000.0170.0030.0860.0070.0170.0000.0000.0170.0090.0330.0100.0000.0530.0000.0000.0000.0060.0000.0000.0050.0000.0000.0230.0000.0000.0030.0130.0000.0000.0000.0310.0170.0140.0140.0190.6070.0000.0140.0000.0000.0000.0160.1130.0000.0050.0000.2350.0000.0000.0220.0220.0000.0000.0240.0000.019
pure-pak0.0000.0000.0000.0000.0000.0000.0030.0140.0300.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.000
plastic,en:metal0.0000.0000.0000.0000.0000.0000.0000.0020.0140.0000.0110.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
sig0.0120.0000.0000.0000.0000.0000.0290.0000.0000.0000.0030.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
light-aluminium0.0000.0000.0430.0000.2380.0000.0780.0050.0810.0000.0080.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.1030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0160.000
pe-7-polyethylene0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0650.0000.000
rpet-recycled-polyethylene-terephthalate0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
pet-colored0.0000.0000.0000.0000.1020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
pet-polyethylene-terephthalate0.0150.0000.0060.0000.0000.0030.0450.0000.0940.0000.0100.0000.0000.0000.0000.0000.0000.0001.0000.0000.0280.0000.0100.0000.0000.0000.0000.0000.0000.0000.0450.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0270.0000.0000.0000.0000.0040.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.000
other-paper0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.1380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
hdpe-high-density-polyethylene0.0000.0000.0000.0000.0000.0000.0180.0000.0490.0000.0170.0000.0000.0000.0000.0000.0880.0000.0280.0001.0000.0000.0000.0000.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.1270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
clear-glass0.0500.0000.0000.0000.0450.0000.0290.0080.0340.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0980.0000.0000.0000.0000.0000.0170.0210.0000.0000.0000.0000.0000.0000.1060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
metal0.0390.0390.0610.1530.0390.0550.1100.0000.3390.0000.0860.0000.0420.0000.0000.0000.0000.0000.0100.0000.0000.0231.0000.0150.0000.0000.0000.0000.0000.0000.0640.0000.0070.0000.0000.0000.0000.0000.0000.0000.0120.0300.0160.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.2180.0000.0190.0000.0000.0000.0000.2850.0000.0000.0000.0240.0000.0000.0320.0000.0000.0000.0000.0000.003
paper0.0140.0060.0630.0080.1500.0260.0890.0000.1820.0000.0070.0000.0000.0000.1030.0000.0000.0000.0000.0180.0000.0000.0151.0000.0000.0180.0040.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0180.0070.0980.0420.0000.0000.0000.0620.0000.0700.0340.1680.0000.0000.0000.0180.0000.2870.0260.0110.0000.0000.0400.0180.0000.0490.0990.0000.0000.0480.0000.000
o-7-other-plastics0.0000.0000.0000.0000.0410.0000.0000.0290.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.5580.0000.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.034
baking-paper0.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
multilayer-composite0.0000.0000.0000.0000.0000.0000.0000.0030.0800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0040.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
recycled-plastic0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.1110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
non-corrugated-cardboard0.0000.0000.0210.0000.0440.0000.0260.0240.0620.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0220.0000.0000.0910.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9180.0000.0000.0000.0000.0120.0380.0000.0000.0340.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.0000.0000.0000.0000.0000.0000.0000.0000.000
other-plastics0.0000.0000.0000.0000.0330.0000.0000.0230.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.1380.0000.0000.0000.0000.5580.0000.0000.0000.0221.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.020
cork0.0000.0000.0000.0000.0000.0000.0240.0000.0410.0000.0100.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0930.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.000
brown-glass0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3530.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
pp-polypropylene0.0250.0000.0570.0000.1440.0090.0940.0120.1530.0000.0530.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0270.0070.0000.0000.0000.0000.0000.0910.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.0190.0000.0000.0000.0000.0130.0000.0000.0000.0980.0000.0000.0000.0000.0000.0000.0090.0030.0000.0000.0170.0000.0000.0000.0000.0240.0000.0000.0000.000
pure-pak-classic0.0000.0000.0000.0000.0000.0000.0030.0140.0300.0000.0000.9170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.000
italpack0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
ldpe-4-low-density-polyethylene0.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
tetra-brik-aseptic0.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.679
pet-opaque0.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5000.0000.0000.0000.0000.0000.0000.000
fsc-cardboard0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0750.000
paper-and-fibreboard-miscellaneous-metals0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
green-glass0.0000.0000.0000.0000.0000.0000.0000.0110.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
tetra-pak0.0230.0000.0330.0000.0230.0100.0560.0140.0640.0000.0230.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0000.0001.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.000
opaque-pet0.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3530.0000.0000.0000.000
fsc-paper0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
corrugated-cardboard0.0000.0000.0230.0000.0410.0000.0260.0220.0600.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0810.0000.0000.9180.0200.0000.0000.0840.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0100.0350.0000.0000.0310.0460.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0960.0000.0000.0000.0000.0000.0000.0000.0000.000
heavy-aluminium0.0000.0000.0410.0440.2400.0000.1070.0190.1450.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0240.0980.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0001.0000.0160.0000.0000.0060.0000.0000.0000.0340.1280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.0000.0000.0000.0000.0000.0000.0000.0000.000
recycled-paper0.0280.0000.0000.0000.0000.0000.0000.0160.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0161.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
pe-polyethylene0.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0980.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
pet-transparent0.0000.0000.0000.0000.0000.0000.0000.0130.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
ps-polystyrene0.0000.0000.0380.0000.0110.0000.0540.0000.0700.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0060.0000.0000.0001.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
plastic-aluminium0.0070.0000.0000.0000.0000.0000.0000.0000.0340.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0620.0000.0000.0000.0000.0380.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0001.0000.0000.7830.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.000
fabric0.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.000
paper-and-cardboard-plastic-aluminium0.0130.0000.0000.0000.0000.0000.0000.0000.0380.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.7830.0001.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.000
pp-5-polypropylene0.0110.0000.0040.0000.1050.0000.0240.0000.0820.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0340.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0340.0480.0000.0000.0000.0000.0000.0001.0000.0460.0000.0000.1080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.000
unknown0.0330.0140.0730.0000.0580.0000.0860.0140.2750.0000.6070.0070.0060.0120.0390.0000.0000.0000.0520.0000.0210.0210.2180.1680.0030.0000.0100.0120.0420.0210.0260.0000.0980.0070.0000.0000.0210.0000.0000.0100.0000.0000.0820.0000.0000.0460.1280.0000.0000.0000.0320.0120.0100.0100.0461.0000.0100.0100.0110.0000.0230.0510.2260.0000.0100.0000.3790.0000.0000.0390.0160.0000.0120.0480.0220.037
tin-plated-steel0.0000.0000.0000.0000.0480.0160.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0101.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2290.0000.000
recyclable-plastic0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0190.0000.0000.0000.0000.1110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
pet-1-polyethylene-terephthalate0.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1080.0110.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
composite-material0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.3530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
900.0000.0000.0000.0000.0000.0000.0160.0000.1160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
paperboard0.0070.0000.0450.0000.0000.0000.0580.0100.0350.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2870.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0000.0000.0000.0000.0001.0000.0060.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.000
glass0.0880.0120.0540.0120.0800.0220.2680.0170.3570.0000.1130.0000.0000.0000.0080.0000.0000.0000.0040.0000.0000.1060.2850.0260.0000.0000.0000.0000.0000.0000.0930.0310.0090.0000.0000.0000.0000.0000.0000.0000.0000.0310.0160.0000.0000.0010.0000.0000.0060.0000.0000.0000.0000.0000.0000.2260.0000.0000.0000.0130.0000.0061.0000.0000.0000.0000.0700.0000.0000.0020.0000.0000.0000.0090.0000.002
ldpe-low-density-polyethylene0.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.000
ps-6-polystyrene0.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
tetra-rex0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
cardboard0.0740.0420.1050.0340.1110.0000.1520.0060.2530.0000.2350.0180.0000.0000.0560.0000.0000.0000.0200.0000.0000.0000.0240.0400.0000.0000.0000.0000.0890.0000.0060.0000.0170.0180.0000.0000.0360.0000.0000.0260.0000.0000.0220.0000.0000.0960.0810.0000.0000.0000.0000.0300.0260.0260.0000.3790.0000.0000.0000.0000.0000.0190.0700.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0180.0000.021
kraft-paper0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
wood0.0000.1280.0940.0930.0000.0170.0460.0120.0230.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
paper-and-plastic0.0000.0000.0000.0000.0340.0000.0000.0000.0260.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0990.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
transparent-pet0.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
elopak0.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
steel0.0000.0000.0000.0150.0120.0150.0310.0000.0820.0000.0240.0000.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.2290.0000.0000.0000.0000.0000.0090.0150.0000.0000.0180.0000.0000.0000.0000.0000.0001.0000.0000.000
hdpe-2-high-density-polyethylene0.0000.0000.0140.0000.0120.0000.0000.0000.0250.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
tetra-brik0.0000.0000.0230.0000.0000.0000.0300.0000.0230.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0340.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.6790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-06-19T06:36:02.112387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-19T06:36:03.341067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

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Duplicate rows

Most frequently occurring

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